European COVID-19 Forecast Hub Evaluation Report
This is an evaluation of forecasts for Covid-19 case and death numbers in 32 European countries submitted to the European COVID-19 Forecast Hub. You can find more information on the European Forecast Hub Github page.
This report is intended as a basic evaluation of forecasts that helps modellers to better understand their performance. The structure and visualisations are likely subject to change in the future and we cannot rule out any mistakes. If you have questions or want to give feedback, please create an issue on our github repository. Note that all forecast dates have been changed to the corresponding submission date (every Monday) to allow easier comparison.
Forecast accuracy
Here is an overview of different evaluation metrics. See below for a more detailed explanation of the scoring metrics used. ‘Overall’ shows scores for all past weeks, ‘latest’ only spans the last 5-6 weeks of data. ‘Detailed’ represents the full data set that you can download for your own analysis.
Austria
overall
latest
Cases
Deaths
overall by horizon
Detailed
Belgium
overall
latest
Cases
Deaths
overall by horizon
Detailed
Bulgaria
overall
latest
Cases
Deaths
overall by horizon
Detailed
Croatia
overall
latest
Cases
Deaths
overall by horizon
Detailed
Cyprus
overall
latest
Cases
Deaths
overall by horizon
Detailed
Czechia
overall
latest
Cases
Deaths
overall by horizon
Detailed
Denmark
overall
latest
Cases
Deaths
overall by horizon
Detailed
Estonia
overall
latest
Cases
Deaths
overall by horizon
Detailed
Finland
overall
latest
Cases
Deaths
overall by horizon
Detailed
France
overall
latest
Cases
Deaths
overall by horizon
Detailed
Germany
overall
latest
Cases
Deaths
overall by horizon
Detailed
Greece
overall
latest
Cases
Deaths
overall by horizon
Detailed
Hungary
overall
latest
Cases
Deaths
overall by horizon
Detailed
Iceland
overall
latest
Cases
Deaths
overall by horizon
Detailed
Ireland
overall
latest
Cases
Deaths
overall by horizon
Detailed
Italy
overall
latest
Cases
Deaths
overall by horizon
Detailed
Latvia
overall
latest
Cases
Deaths
overall by horizon
Detailed
Liechtenstein
overall
latest
Cases
Deaths
overall by horizon
Detailed
Lithuania
overall
latest
Cases
Deaths
overall by horizon
Detailed
Luxembourg
overall
latest
Cases
Deaths
overall by horizon
Detailed
Malta
overall
latest
Cases
Deaths
overall by horizon
Detailed
Netherlands
overall
latest
Cases
Deaths
overall by horizon
Detailed
Norway
overall
latest
Cases
Deaths
overall by horizon
Detailed
Poland
overall
latest
Cases
Deaths
overall by horizon
Detailed
Portugal
overall
latest
Cases
Deaths
overall by horizon
Detailed
Romania
overall
latest
Cases
Deaths
overall by horizon
Detailed
Slovakia
overall
latest
Cases
Deaths
overall by horizon
Detailed
Slovenia
overall
latest
Cases
Deaths
overall by horizon
Detailed
Spain
overall
latest
Cases
Deaths
overall by horizon
Detailed
Sweden
overall
latest
Cases
Deaths
overall by horizon
Detailed
Switzerland
overall
latest
Cases
Deaths
overall by horizon
Detailed
United Kingdom
overall
latest
Cases
Deaths
overall by horizon
Detailed
Evaluation metrics
- Relative skill is a metric based on the weighted interval score (WIS) that is using a ‘pairwise comparison tournament’. All pairs of forecasters are compared against each other in terms of the weighted interval score. The mean score of both models based on the set of common targets for which both models have made a prediction are calculated to obtain mean score ratios. The relative skill is the geometric mean of these mean score ratios. Smaller values are better and a value smaller than one means that the model beats the average forecasting model.
- The weighted interval score is a proper scoring rule (meaning you can’t cheat it) suited to scoring forecasts in an interval format. It has three components: sharpness, underprediction and overprediction. Sharpness is the width of your prediction interval. Over- and underprediction only come into play if the prediction interval does not cover the true value. They are the absolute value of the difference between the upper or lower bound of your prediction interval (depending on whether your forecast is too high or too low).
- coverage deviation is the average difference between nominal and empirical interval coverage. Say your 50 percent prediction interval covers only 20 percent of all true values, then your coverage deviation is 0.5 - 0.2 = -0.3. The coverage deviation value in the table is calculated by averaging over the coverage deviation calculated for all possible prediction intervals. If the value is negative you have covered less then you should. If it is positve, then your forecasts could be a little more confident.
- bias is a measure between -1 and 1 that expresses your tendency to underpredict (-1) or overpredict (1). In contrast to the over- and underprediction components of the WIS it is bound between -1 and 1 and cannot go to infinity. It is therefore less susceptible to outliers.
- aem is the absolute error of your median forecasts. A high aem means your median forecasts tend to be far away from the true values.
Scores over time
Here you can see a visualisation of forecaster scores over time. The first tab shows the weighted interval score. Other tabs show the components of the interval score, sharpness (how narrow are forecasts - smaller is better), and penalties for underprediction and overprediction.
Austria
Weighted interval score
Overprediction
Underprediction
Sharpness
Belgium
Weighted interval score
Overprediction
Underprediction
Sharpness
Bulgaria
Weighted interval score
Overprediction
Underprediction
Sharpness
Croatia
Weighted interval score
Overprediction
Underprediction
Sharpness
Cyprus
Weighted interval score
Overprediction
Underprediction
Sharpness
Czechia
Weighted interval score
Overprediction
Underprediction
Sharpness
Denmark
Weighted interval score
Overprediction
Underprediction
Sharpness
Estonia
Weighted interval score
Overprediction
Underprediction
Sharpness
Finland
Weighted interval score
Overprediction
Underprediction
Sharpness
France
Weighted interval score
Overprediction
Underprediction
Sharpness
Germany
Weighted interval score
Overprediction
Underprediction
Sharpness
Greece
Weighted interval score
Overprediction
Underprediction
Sharpness
Hungary
Weighted interval score
Overprediction
Underprediction
Sharpness
Iceland
Weighted interval score
Overprediction
Underprediction
Sharpness
Ireland
Weighted interval score
Overprediction
Underprediction
Sharpness
Italy
Weighted interval score
Overprediction
Underprediction
Sharpness
Latvia
Weighted interval score
Overprediction
Underprediction
Sharpness
Liechtenstein
Weighted interval score
Overprediction
Underprediction
Sharpness
Lithuania
Weighted interval score
Overprediction
Underprediction
Sharpness
Luxembourg
Weighted interval score
Overprediction
Underprediction
Sharpness
Malta
Weighted interval score
Overprediction
Underprediction
Sharpness
Netherlands
Weighted interval score
Overprediction
Underprediction
Sharpness
Norway
Weighted interval score
Overprediction
Underprediction
Sharpness
Poland
Weighted interval score
Overprediction
Underprediction
Sharpness
Portugal
Weighted interval score
Overprediction
Underprediction
Sharpness
Romania
Weighted interval score
Overprediction
Underprediction
Sharpness
Slovakia
Weighted interval score
Overprediction
Underprediction
Sharpness
Slovenia
Weighted interval score
Overprediction
Underprediction
Sharpness
Spain
Weighted interval score
Overprediction
Underprediction
Sharpness
Sweden
Weighted interval score
Overprediction
Underprediction
Sharpness
Switzerland
Weighted interval score
Overprediction
Underprediction
Sharpness
United Kingdom
Weighted interval score
Overprediction
Underprediction
Sharpness
WIS decomposition
As mentionend above, the weighted interval score can be decomposed into three parts: sharpness (the amount of uncertainty around the forecast), overprediction and underprediction. This visualisation gives an impression of the distribution between these three forms of penalties for the different models.
overall
Austria
Belgium
Bulgaria
Croatia
Cyprus
Czechia
Denmark
Estonia
Finland
France
Germany
Greece
Hungary
Iceland
Ireland
Italy
Latvia
Liechtenstein
Lithuania
Luxembourg
Malta
Netherlands
Norway
Poland
Portugal
Romania
Slovakia
Slovenia
Spain
Sweden
Switzerland
United Kingdom
If you want to learn more about a model, you can go the the ‘data-processed’-folder of the European Forecast Hub github repository, select a model and access the metadata file with further information provided by the model authors.